U.S. patent application number 14/636847 was filed with the patent office on 2016-05-12 for system and method for estimating state of health using battery model parameter.
The applicant listed for this patent is HYUNDAI MOTOR COMPANY. Invention is credited to Woo Suk Sung.
Application Number | 20160131715 14/636847 |
Document ID | / |
Family ID | 55803017 |
Filed Date | 2016-05-12 |
United States Patent
Application |
20160131715 |
Kind Code |
A1 |
Sung; Woo Suk |
May 12, 2016 |
SYSTEM AND METHOD FOR ESTIMATING STATE OF HEALTH USING BATTERY
MODEL PARAMETER
Abstract
A method and system for estimating a state of health using
battery model parameters are provided. The system includes a
battery model parameter extractor that is configured to extract
liquid-phase diffusivity of Li-ion parameters and a storage unit
that is configured to store a mapping table in which states of
health (SOH) for each liquid-phase diffusivity of Li-ion parameter
are mapped. In addition, a SOH estimator is configured to use the
mapping table to estimate the SOH that corresponds to a
liquid-phase diffusivity of Li-ion extracted from the battery model
parameter extractor.
Inventors: |
Sung; Woo Suk; (Bucheon,
KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
HYUNDAI MOTOR COMPANY |
Seoul |
|
KR |
|
|
Family ID: |
55803017 |
Appl. No.: |
14/636847 |
Filed: |
March 3, 2015 |
Current U.S.
Class: |
702/63 |
Current CPC
Class: |
G01R 31/382 20190101;
H01M 10/052 20130101; G01R 31/392 20190101; H01M 10/48 20130101;
H01M 10/0566 20130101; G01R 31/367 20190101; Y02E 60/10
20130101 |
International
Class: |
G01R 31/36 20060101
G01R031/36 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 6, 2014 |
KR |
10-2014-0153938 |
Claims
1. A system for estimating a state of health (SOH) using battery
model parameters, comprising: a battery model parameter extractor
configured to extract liquid-phase diffusivity of lithium ion
(Li-ion) parameters; a storage unit configured to store a mapping
table in which the states of health for each liquid-phase
diffusivity of Li-ion parameter are mapped; and a SOH estimator
configured to use the mapping table to estimate the SOH that
corresponds to liquid-phase diffusivity of Li-ion extracted from
the battery model parameter extractor.
2. The system according to claim 1, wherein the liquid-phase
diffusivity of Li-ion parameter includes liquid-phase diffusivity
of Li-ion in a liquid electrolyte.
3. The system according to claim 1, wherein the mapping table
includes rated capacities and capacity fade of the battery which
are mapped to each liquid-phase diffusivity of Li-ion parameter
value.
4. The system according to claim 3, wherein the SOH is calculated
by dividing current rated capacity of a battery by initial rated
capacity of the battery.
5. The system according to claim 1, wherein the battery model
parameter extractor is configured to collect voltage data during
charging and when the charging is completed and extract the
liquid-phase diffusivity of Li-ion parameter using the voltage
data.
6. A method for estimating a state of health (SOH) using battery
model parameters, comprising: determining, by a controller, whether
a battery begins to be charged; collecting, by the controller, data
for parameter extraction when the battery begins to be charged;
extracting, by the controller, a liquid-phase diffusivity of
lithium ion (Li-ion) parameter using the data when the charging of
the battery ends; and estimating, by the controller, the state of
health using the liquid-phase diffusivity of Li-ion parameter.
7. The method according to claim 6, further comprising: prior to
the determining whether the battery begins to be charged,
generating and storing, by the controller, a mapping table in which
SOHs for each liquid-phase diffusivity of Li-ion parameter value
are mapped by calculating the SOHs for each liquid-phase
diffusivity of Li-ion parameter based on an experiment.
8. The method according to claim 7, wherein in the generating and
storing of the mapping table, rated capacities of the battery for
each liquid-phase diffusivity of Li-ion parameter are calculated
and the SOH is calculated by dividing current rated capacity of the
battery by initial rated capacity of the battery and stored.
9. The method according to claim 6, wherein the liquid-phase
diffusivity of Li-ion parameter includes a liquid-phase diffusivity
of Li-ion in a liquid electrolyte.
10. A non-transitory computer readable medium containing program
instructions executed by a controller, the computer readable medium
comprising: program instructions that determine whether a battery
begins to be charged; program instructions that collect data for
parameter extraction when the battery begins to be charged; program
instructions that extract a liquid-phase diffusivity of lithium ion
(Li-ion) parameter using the data when the charging of the battery
ends; and program instructions that estimate the state of health
using the liquid-phase diffusivity of Li-ion parameter.
11. The non-transitory computer readable medium of claim 10,
further comprising: program instructions that generate and store a
mapping table in which SOHs for each liquid-phase diffusivity of
Li-ion parameter value are mapped by calculating the SOHs for each
liquid-phase diffusivity of Li-ion parameter based on an
experiment, prior to the determining whether the battery begins to
be charged.
12. The non-transitory computer readable medium of claim 11,
wherein in the generating and storing of the mapping table, rated
capacities of the battery for each liquid-phase diffusivity of
Li-ion parameter are calculated and the SOH is calculated by
dividing current rated capacity of the battery by initial rated
capacity of the battery and stored.
13. The non-transitory computer readable medium of claim 10,
wherein the liquid-phase diffusivity of Li-ion parameter includes a
liquid-phase diffusivity of Li-ion in a liquid electrolyte.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is based on and claims the benefit of
priority to Korean Patent Application No. 10-2014-0153938, filed on
Nov. 6, 2014 in the Korean Intellectual Property Office, the
disclosure of which is incorporated herein in its entirety by
reference.
TECHNICAL FIELD
[0002] The present disclosure relates to a system and method for
estimating a state of health (SOH) to using battery model
parameters, and more particularly, to a technology of estimating a
state of health using a liquid-phase diffusivity of Li-ion
parameter having high correlation with a deterioration of the
battery among battery model parameters.
BACKGROUND
[0003] As the problem of environmental pollution increases, the
competition among different technical fields to develop
environmentally-friendly energy is increasing. In addition, the
competition to develop alternative sources of energy due to a
depletion of petroleum and natural resources, and the like has
increased. Accordingly, the competition among vehicle manufacturers
to develop next-generation vehicles is increasing. Among the
next-generation vehicles, there are pure electric vehicles (EV)
which use a battery as an energy source and an engine hybrid
electric vehicle (HEV), a fuel cell hybrid electric vehicle, and
the like, which use a battery as an energy buffer.
[0004] In the hybrid vehicle, a battery system is one of main parts
that determines a quality of a vehicle. The battery system of the
hybrid vehicle is an auxiliary energy source which assists an
output of an engine and accumulates generated energy while the
vehicle is driven. In particular, a control technology of the
battery system is important which includes a power control,
cooling, diagnosis, a state of charge (SOC) calculation, and the
like.
[0005] Among those control technologies, the state of charge
calculation is an important factor for driving strategy of a
vehicle. In other words, in the hybrid vehicle, the state of charge
is calculated to operate a battery to be charged when additional
energy is generated and operate the vehicle to obtain a required
output by discharging the battery when a substantially high output
is required. Therefore, to reduce energy and maximize operation
efficiency of the hybrid vehicle by accurately implementing the
driving strategy of the hybrid vehicle, there is a need to more
accurately calculate the state of charge.
[0006] When the state of charge calculation is inaccurate, the
operation efficiency of the hybrid vehicle may be reduced and
dangerous situations may occur. For example, when the actual state
of charge is 80% but the calculated state of charge is 30%, a
vehicle controller is configured to determine to that the charging
is required, and thus the battery may be overcharged or in the
opposite case, the battery may be overdischarged. Ignition or
explosion of the battery may occur due to the overcharging or the
overdischarging, and thus dangerous situations may occur.
[0007] For the state of charge calculation, battery degradation,
that is, a state of health is provided as a main input. The state
of health is increasing degraded based on use environment or a use
period of time than an early production of a battery, and thus an
available capacity is reduced or resistance is increased.
Generally, the state of health is degraded to about 20%. To prevent
the reduction in energy and the risk by efficiently operating the
hybrid vehicle, there is a need to more accurately estimate the
state of health.
[0008] The existing method for estimating a state of health may
vary, but may be divided into two methods. A first method is a
method for directly applying a load having a predetermined
frequency configuring hardware to the battery to measure impedance
thereof and understand the state of health. A second method is a
method for acquiring current and voltage pair data for a
predetermined period to infer indirect impedance or a degraded
capacity.
[0009] The first method for using hardware may have reduced
efficiency due to errors, durability, costs, and the like of a
method for configuring a circuit thereof and sensors. The second
method may be difficult to implement the accurate inference or may
have complex logic due to strong nonlinearity and disturbances from
the method for acquiring the current and voltage pair data to the
method for inferring the indirect impedance and the degraded state
of charge.
[0010] Accordingly, a method for estimating a state of health by
calculating charging capacity within a specific voltage section
based on a current accumulation is developed, which may be applied
at room temperature and during slow charging and may be vulnerable
to accumulated errors of a current sensor during the current
accumulation.
SUMMARY
[0011] The present provides a system for estimating a state of
health using battery model parameters and a method thereof capable
of estimating the state of health using a liquid-phase diffusivity
of lithium ion (Li-ion) parameter having a high correlation with
capacity fade of the battery among the battery model
parameters.
[0012] According to an exemplary embodiment of the present
disclosure, a system for estimating a state of health using battery
model parameters may include a battery model parameter extractor
configured to extract liquid-phase diffusivity of Li-ion
parameters; a storage unit configured to store a mapping table in
which states of health for each liquid-phase diffusivity of Li-ion
parameter are mapped; and an state of health (SOH) estimator
configured to use the mapping table to estimate the SOH that
corresponds to liquid-phase diffusivity of Li-ion extracted from
the battery model parameter extractor.
[0013] The liquid-phase diffusivity of Li-ion parameter may include
liquid-phase diffusivity of Li-ion in a liquid electrolyte. The
mapping table may include capacities and capacity fades of the
battery mapped to each liquid-phase diffusivity of Li-ion parameter
value. The SOH may be calculated by dividing current rated capacity
of the battery by initial rated capacity of the battery. The
battery model parameter extractor may be configured to collect
voltage data during the charging and when the charging is
completed, extract the liquid-phase diffusivity of Li-ion parameter
using the voltage data.
[0014] According to another exemplary embodiment of the present
disclosure, a method for estimating a state of health using battery
model parameters may include: determining whether a battery begins
to be charged; collecting data for parameter extraction when the
battery begins to be charged; extracting a liquid-phase diffusivity
of Li-ion parameter using the data when the charging of the battery
ends; and estimating the state of health using the liquid-phase
diffusivity of Li-ion parameter.
[0015] The method may further include: prior to the determining
whether a battery begins to be charged, generating and storing a
mapping table in which SOHs for each liquid-phase diffusivity of
Li-ion parameter value are mapped by calculating the SOHs for each
liquid-phase diffusivity of Li-ion parameter based on an
experiment. In the generating and storing of the mapping table,
rated capacities of the battery for each liquid-phase diffusivity
of Li-ion parameter may be calculated and the SOH may be calculated
by dividing rated capacity of the battery by initial rated capacity
of the battery and stored. The liquid-phase diffusivity of Li-ion
parameter may include a liquid-phase diffusivity of Li-ion in a
liquid electrolyte.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and other objects, features and advantages of the
present disclosure will be more apparent from the following
detailed description taken in conjunction with the accompanying
drawings.
[0017] FIG. 1 is an exemplary graph illustrating a change in rated
capacity of a battery based on a use of a battery according to an
exemplary embodiment of the present disclosure;
[0018] FIG. 2 is an exemplary configuration diagram of a system for
estimating a state of health using battery model parameters
according to an exemplary embodiment of the present disclosure;
[0019] FIG. 3A is an exemplary internal structure diagram of a
battery configured to include a positive electrode, a separator,
and a negative electrode according to the exemplary embodiment of
the present disclosure;
[0020] FIG. 3B is an exemplary diagram for describing a movement of
lithium ions according to the exemplary embodiment of the present
disclosure;
[0021] FIG. 3C is an exemplary diagram for describing battery model
parameters according to the exemplary embodiment of the present
disclosure;
[0022] FIG. 4 is an exemplary graph illustrating a change in
parameters based on capacity fades for each parameter according to
the exemplary embodiment of the present disclosure;
[0023] FIG. 5 is an exemplary graph for describing a reason of
using a liquid-phase diffusivity of Li-ion parameter according to
the exemplary embodiment of the present disclosure;
[0024] FIG. 6 is an exemplary table in which a relationship between
the liquid-phase diffusivity of Li-ion parameter according to the
exemplary embodiment of the present disclosure and a state of
health (SOH) is mapped; and
[0025] FIG. 7 is an exemplary flow chart of a method for estimating
a state of health using battery to model parameters according to an
exemplary embodiment of the present disclosure.
DETAILED DESCRIPTION
[0026] It is understood that the term "vehicle" or "vehicular" or
other similar term as used herein is inclusive of motor vehicles in
general such as passenger automobiles including sports utility
vehicles (SUV), buses, trucks, various commercial vehicles,
watercraft including a variety of boats and ships, aircraft, and
the like, and includes hybrid vehicles, electric vehicles, plug-in
hybrid electric vehicles, hydrogen-powered vehicles and other
alternative fuel vehicles (e.g. fuels derived from resources other
than petroleum). As referred to herein, a hybrid vehicle is a
vehicle that has two or more sources of power, for example both
gasoline-powered and electric-powered vehicles.
[0027] Although exemplary embodiment is described as using a
plurality of units to perform the exemplary process, it is
understood that the exemplary processes may also be performed by
one or plurality of modules. Additionally, it is understood that
the term controller/control unit refers to a hardware device that
includes a memory and a processor. The memory is configured to
store the modules and the processor is specifically configured to
execute said modules to perform one or more processes which are
described further below.
[0028] Furthermore, control logic of the present invention may be
embodied as non-transitory computer readable media on a computer
readable medium containing executable program instructions executed
by a processor, controller/control unit or the like. Examples of
the computer readable mediums include, but are not limited to, ROM,
RAM, compact disc (CD)-ROMs, magnetic tapes, floppy disks, flash
drives, smart cards and optical data storage devices. The computer
readable recording medium can also be distributed in network
coupled computer systems so that the computer readable media is
stored and executed in a distributed fashion, e.g., by a telematics
server or a Controller Area Network (CAN).
[0029] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof. As
used herein, the term "and/or" includes any and all combinations of
one or more of the associated listed items.
[0030] Unless specifically stated or obvious from context, as used
herein, the term "about" is understood as within a range of normal
tolerance in the art, for example within 2 standard deviations of
the mean. "About" can be understood as within 10%, 9%, 8%, 7%, 6%,
5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated
value. Unless otherwise clear from the context, all numerical
values provided herein are modified by the term "about."
[0031] Hereinafter, exemplary embodiments of the present disclosure
will be described in detail with reference to the accompanying
drawings so that those skilled in the art may easily implement the
spirit of the present disclosure.
[0032] A state of health (SOH) is a parameter quantitatively
representing a change in characteristics of rated capacity of the
battery due to an aging effect and may appreciate how much the
rated capacity of the battery is degraded. Therefore, upon
determining the state of health, it may be possible to replace a
battery at an appropriate time and prevent the battery from being
overcharged and overdischarged by adjusting charging and
discharging capacity of the battery while the battery is being used
Therefore, the exemplary embodiment of the present disclosure
discloses a technology of more accurately estimating the SOH.
[0033] Hereinafter, exemplary embodiments of the present disclosure
will be described with reference to FIGS. 1 to 7.
[0034] A battery for a hybrid vehicle or an electric vehicle may be
repeatedly charged and discharged while the vehicle is driven and
may be configured to supply power to drive the vehicle. However, as
a use period of the battery for the vehicle increases, chargeable
capacity may decrease. Referring to FIG. 1, the chargeable capacity
of the battery may gradually decrease each time the battery is
used. In other words, it may be appreciated that when a cycle is 0,
the chargeable capacity of the battery is 100, when the cycle
becomes 500, the chargeable capacity of the battery is reduced to
97.5, when the cycle becomes 1000, the chargeable capacity of the
battery is 93.7, and when the cycle becomes 2500, the chargeable
capacity of the battery is reduced to 85. Accordingly, when the
chargeable capacity of the battery is reduced, a driving distance
may be reduced even though the battery is charged. Therefore, the
system for estimating a state of health using battery model
parameter according to the exemplary embodiment of the present
disclosure may more accurately estimate the chargeable capacity of
the battery, that is, the state of health (SOH).
[0035] FIG. 2 is an exemplary configuration diagram of a system for
estimating a state of health using battery model parameters
according to an exemplary embodiment of the present disclosure.
Hereinafter, the state of health is referred to as an SOH. The
system for estimating a state of health using battery model
parameters according to an exemplary embodiment of the present
disclosure may include a battery 200 and a battery management unit
100, in which the battery management unit 100 may include a battery
model parameter extractor 110, an SOH estimator 120, and a storage
unit 130. The battery 200 and battery management unit 100 with the
components thereof may be operated by a controller.
[0036] The battery management unit 100 may be implemented as a
battery management system (BMS) configured to manage an SOH, a
state of charge (SOC), and the like of a vehicle battery. A
configuration of a general battery management system in addition to
the configuration for estimating an SOH which is a feature of the
present disclosure will be omitted. The battery model parameter
extractor 110 may be configured to extract the battery model
parameters associated with capacity fade when the battery 200
begins to be charged (e.g., when charging begins). The battery
model parameter associated with the capacity fade may include
liquid electrolyte Li+ diffusivity De, solid + electrode Li+
diffusivity Dsp, solid - electrode Li+ diffusivity Dsn, a
+electrode reaction rate coefficient Kp, and a - electrode reaction
rate coefficient Kn.
[0037] The above parameters are associated with the deterioration
of the battery. As illustrated in FIG. 3A, the battery may include
a positive electrode 10, a negative electrode 20, and a separator
30 disposed between the positive electrode 10 and the negative
electrode 20, in which an electrolyte 40 may flow in the separator
30. Referring to FIG. 3B, the lithium ion may be separated from the
negative electrode 20 to flow in the positive electrode 10 through
the separator 30, in which the solid -electrode Li+ diffusivity Dsn
parameter represents a liquid-phase diffusivity of Li-ion at the
negative electrode 20, the solid + electrode Li+ diffusivity Dsp
parameter represents the liquid-phase diffusivity of Li-ion at the
positive electrode 10, and the liquid electrolyte Li+ diffusivity
De parameter represents the liquid-phase diffusivity of Li-ion at
the separator 30. Referring to FIG. 3C, a reaction formula and a
reaction rate coefficient K based on the movement of the lithium
ion are represented.
[0038] However, FIG. 4 illustrates a change in parameter values for
each capacity fade of the above five parameters De, Dsp, Dsn, Kp,
and Kn, in which it may be appreciated that the solid + electrode
Li+diffusivity Dsp, the solid -electrode Li+ diffusivity Dsn, and
the +electrode reaction rate coefficient Kp do not have a constant
graph curve but are changed, while the liquid electrolyte Li+
diffusivity De and the - electrode reaction rate coefficient Kn are
constantly exponentially changed.
[0039] However, referring to FIG. 5, a graph representing capacity
fade for one liquid electrolyte Li+diffusivity De is similar to a
graph representing capacity fade by a combination of the five
parameters. Since the five parameters are combined, the algorithm
is complex and a substantial amount of time is required, the
exemplary embodiment of the present disclosure uses liquid
electrolyte Li+ diffusivity De, which is one parameter, to estimate
the SOH.
[0040] In other words, when the battery deteriorates, phenomena
such as agglomeration of solid electrode particle, formation of the
solid-electrolyte interface layer, lithium metal deposition,
mechanical cracking due to fatigue stress, and active material
dissolution may occur. However, when the above phenomena occur, the
liquid-phase diffusivity of Li-ion may be reduced and thus an
electrochemical reaction rate may decrease. Since electrons flow
through an external conducting wire, the flow of electrons is rapid
and therefore resistance is not required to be considered and since
the flow of most lithium ions is hindered within the battery, the
liquid-phase diffusivity of Li-ion may be reduced.
[0041] Referring to a mapping table of FIG. 6, it may be
appreciated that as the capacity fade of the battery increases, the
liquid-phase diffusivity of Li-ion may decrease. The SOH estimator
120 may be configured to use the mapping table stored in the
storage unit 130 to confirm the SOH mapped to the parameter values
extracted from the battery model parameter extractor 110, to thus
estimate the SOH. In particular, the SOH may indicate how much the
deterioration or the reduction in rated capacity of the battery
proceeds (e.g., an amount of deterioration).
[0042] The storage unit 130 may be configured to store a mapping
table as illustrated in FIG. 6 in which the lithium ion speed, the
rated capacity of the battery, the capacity fade, and the SOH are
mapped. Particularly, the SOH may be calculated by the following
Equation 1.
SOH ( % ) = Current Rated Capacity Of Battery Initial Rated
Capacity Of Battery * 100 Equation 1 ##EQU00001##
[0043] In FIG. 6, the capacity fade may refer to a quantity
obtained by subtracting the SOH from 100. The SOH estimator 120 may
be configured to measure the rated capacities of the battery for
each liquid-phase diffusivity of Li-ion in advance based on an
experiment value and calculate the capacity fade and the SOH to
generate the mapping table as illustrated in FIG. 6 and store the
generated mapping table in the storage unit 130. Hereinafter, a
method for estimating a state of health using battery model
parameters according to an exemplary embodiment of the present
disclosure will be described in detail with reference to FIG. 7.
First, it may be assumed that the mapping table as illustrated in
FIG. 6 is stored in the storage unit 130.
[0044] The battery model parameter extractor 110 may be configured
to determine whether the battery 200 begins to be charged (e.g.,
determine a start of battery charging) (S101) and collect data when
the battery 100 begins to be charged (S102) which may include
voltage data. Further, the battery model parameter extractor 110
may be configured to determine whether the charging ends (S103) and
when the charging ends, the liquid-phase diffusivity of Li-ion
parameters may be extracted using the collected data (S104). In
particular, the extraction of the liquid-phase diffusivity of
Li-ion parameters may refer to the calculation of the liquid-phase
diffusivity of Li-ion in the liquid electrolyte and as the method
for extracting parameters using data during the charging, a general
method may be applied, and therefore the detailed description
thereof will be omitted. Therefore, the SOH estimator 120 may be
configured to extract the SOH that corresponds to the liquid-phase
diffusivity of Li-ion calculated by referring to the mapping table
stored in the storage unit 130 to estimate the SOH (S105).
[0045] The exemplary embodiment of the present disclosure may use
the liquid-phase diffusivity of Li-ion parameters in the liquid
electrolyte to more accurately extract the SOH to provide the more
accurate SOH to the system for performing the state of charge
calculation, and the like, using the SOH, to thus improve the
reliability of various types of control devices using the SOH such
as the state of charge, and the like.
[0046] According to the exemplary embodiments of the present
disclosure, it may be possible to more accurately estimate the
state of health (SOH) independent of the charging speed and the
temperature. Further, according to the exemplary embodiments of the
present disclosure, it may be possible to improve the reliability
of the control technology in addition to the technology of securing
the reliability of the battery model, calculating the state of
charge (SOC) having a close relation with the state of health, and
the like, by more accurately estimating the state of health.
[0047] The exemplary embodiments of the present disclosure
described above have been provided for illustrative purposes.
Therefore, those skilled in the art will appreciate that various
modifications, alterations, substitutions, and additions are
possible without departing from the scope and spirit of the present
disclosure as disclosed in the accompanying claims and such
modifications, alterations, substitutions, and additions fall
within the scope of the present disclosure.
* * * * *